For example, in a manufacturing line of wire rod, a defect classification apparatus is used with image processing on a grabbed image sequence of wire rod grabbed by a camera placed in the manufacturing line to classify defect types of a defect such as a crack on the wire rod. The defect classification apparatus determines a defect region corresponding to a defect that has occurred in a wire rod from in the grabbed image sequence, and classify the defect type of the defect from features (such as the size and area) of the defect region.
In the defect classification apparatus of Patent Literature 1, a mapping space, which is a higher dimension than feature information (a vector) whose components include a plurality of features (for example, the size and the area of a defect) indicating attributes of a defect to be classified having an unknown defect type is classified into two regions of defect type by a decision boundary. This decision boundary is created in priori by the defect classification apparatus of Patent Literature 1 by using the feature information of a training defect dataset of the two defect types of which defect type is labeled by a user. In the defect classification apparatus of Patent Literature 1, a data point (a point at the tip of a vector) indicating feature information of a defect to be classified is mapped into the mapping space, and unknown defect type of the defect to be classified will be classified to the defect type corresponding to the region where the mapped data point (hereafter, referred to as a “mapped point”) is located.
The defect classification apparatus of Patent Literature 1 creates a decision boundary such that when the defect type of each training defect dataset is classified, the defect type of each training defect dataset is correctly classified (as classified by a user) by a similar method (a method of mapping a data point indicating feature information into a mapping space, and classifying the defect type in accordance with the region in which the mapped point is located) as that for classification of the defect types of a defect to be classified. Among training defect dataset used for the creation of a decision boundary, there is a training defect dataset whose feature has a singular value, and the position of mapped point of which is significantly different from that of a training defect dataset whose feature does not have a singular value. The defect classification apparatus of Patent Literature 1 creates a decision boundary such that the defect type of each training defect dataset is correctly classified. As a result, a decision boundary which is over-fitted to the feature information of the training defect dataset which is used for creating the decision boundary will have been created, resulting in over-fitting which is a phenomenon that the ability to cope with a defect to be classified having an unknown defect type is deteriorated. If over-fitting occurs when there is a training defect dataset whose feature has a singular value, a decision boundary having an overly increased dimensional number is created such that the defect type is accurately judged even for the training defect dataset whose feature has a singular value. If such over-fitting occurs, there may be a case in which the defect type of a defect to be classified having an unknown defect type cannot be accurately classified.